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Application of Artificial Neural Networks to Power Systems

Definitions

The computational neuroscience has three goals:

1. the computer aided simulation of some functionalities of the brain,
2. the understanding of the function of the brain in computational terms,
3. the application of neural concepts for innovative technical problem solving.

There is no universally accepted definition of an ANN. As I would not like to create just another one, below is a sampling definitions from [I1, I2, I6, I26]:

According to the DARPA Neural Network Study (1988), AFCEA International Press, p. 60:

... a neural network is a system composed of many simple processing elements operating in parallel whose function is determined by network structure, connection strengths, and the processing performed at computing elements or nodes.

According to Zurada, J.M. (1992), Introduction To Artificial Neural Systems, Boston, PWS Publishing Company, p. xv:

Artificial neural systems, or neural networks, are physical cellular systems which can acquire, store, and utilize experiential knowledge.

According to Nigrin, A. (1993), Neural Networks for Pattern Recognition, Cambridge, MA: The MIT Press, p. 11:

A neural network is a circuit composed of a very large number of simple processing elements that are neurally based. Each element operates only on local information. Furthermore each element operates asynchronously; thus there is no overall system clock.

According to Haykin, S. (1994), Neural Networks: A Comprehensive Foundation, NY: Macmillan, p. 2:

A neural network is a massively parallel distributed processor that has a natural propensity for storing experiential knowledge and making it available for use. It resembles the brain in two respects:

1. Knowledge is acquired by the network through a learning process.
2. Interneuron connection strengths known as synaptic weights are used to store the knowledge.

In an on-line article Neural Nets by Kevin Gurney, (1996):

A Neural Network is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns.

According to Sarle, W. S. (1997), Neural Network FAQ:

...in most of souces the ANNs are considered as networks of many simple processors ("processing elements"), each possibly having a small amount of local memory. The elements are connected by communication channels ("connections") which usually carry numeric (as opposed to symbolic) data, encoded by any of various means. The elements operate only on their local data and on the inputs they receive via the connections. The restriction to local operations is often relaxed during training.

According to Leslie S. Smith's (1997) on-line introduction:

Neural networks are a form of multiprocessor computer system, with

• simple processing elements,
• a high degree of interconnection,
• simple scalar messages,
• adaptive interaction between elements.

And finally the longest one from FOLDOC: Free On-Line Dictionary of Computing, (1998):

An ANN is a network of many very simple processors ("units" or "neurons"), each possibly having a (small amount of) local memory. The units are connected by unidirectional communication channels ("connections"), which carry numeric (as opposed to symbolic) data. The units operate only on their local data and on the inputs they receive via the connections. A neural network is a processing device, either an {algorithm}, or actual hardware, whose design was motivated by the design and functioning of animal brains and components thereof.

As noted in the previous chapter, neurocomputing is one of fastest growing areas of research in the field of AI. At least the number of definitions grows rapidly.

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